U.S. patent application number 16/080135 was filed with the patent office on 2019-02-14 for methods and apparatus for edge surface inspection of a moving glass web.
The applicant listed for this patent is CORNING INCORPORATED. Invention is credited to David Joseph Kuhn, Philip Robert LeBlanc, AJayantha Senawiratne, Weihua Sun.
Application Number | 20190047895 16/080135 |
Document ID | / |
Family ID | 58261739 |
Filed Date | 2019-02-14 |
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United States Patent
Application |
20190047895 |
Kind Code |
A1 |
Kuhn; David Joseph ; et
al. |
February 14, 2019 |
METHODS AND APPARATUS FOR EDGE SURFACE INSPECTION OF A MOVING GLASS
WEB
Abstract
Methods and apparatus provide for sourcing a glass web, the
glass web having a length and a width transverse to the length;
moving the glass web from the source to a destination in a
transport direction along the length of the glass web; cutting the
glass web, at a cutting zone, along the length of the glass web
into at least first and second glass ribbons as the glass web is
moved in the transport direction from the source to the
destination, such that respective first and second edge surfaces
are produced on the first and second glass ribbons; and optically
inspecting at least one of the first and second edge surfaces in
real-time as the first and second glass ribbons of the glass web
are moved in the transport direction to the destination.
Inventors: |
Kuhn; David Joseph;
(Prattsburgh, NY) ; LeBlanc; Philip Robert;
(Corning, NY) ; Senawiratne; AJayantha;
(Horseheads, NY) ; Sun; Weihua; (Corning,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CORNING INCORPORATED |
CORNING |
NY |
US |
|
|
Family ID: |
58261739 |
Appl. No.: |
16/080135 |
Filed: |
February 23, 2017 |
PCT Filed: |
February 23, 2017 |
PCT NO: |
PCT/US2017/019006 |
371 Date: |
August 27, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62299750 |
Feb 25, 2016 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B65H 2801/61 20130101;
G06T 2207/30108 20130101; B65H 2301/4148 20130101; C03B 33/0235
20130101; C03B 33/037 20130101; G06T 2207/30124 20130101; G01N
21/896 20130101; C03B 33/091 20130101; G06T 7/0004 20130101 |
International
Class: |
C03B 33/023 20060101
C03B033/023; C03B 33/09 20060101 C03B033/09; G01N 21/896 20060101
G01N021/896; G06T 7/00 20060101 G06T007/00 |
Claims
1. A method, comprising: moving a glass web comprising a length and
a width transverse to the length from a source to a destination in
a transport direction along the length of the glass web; cutting
the glass web, at a cutting zone, along the length of the glass web
into at least first and second glass ribbons as the glass web is
moved in the transport direction from the source to the destination
such that respective first and second edge surfaces are produced on
the first and second glass ribbons; and optically inspecting at
least one of the first and second edge surfaces in real-time as the
first and second glass ribbons are moved in the transport direction
to the destination, wherein the inspecting step includes: (i)
taking at least one image of the at least one of the first and
second edge surfaces as the first and second glass ribbons are
moved in the transport direction, (ii) extracting one or more
features of the at least one of the first and second edge surfaces
from the at least one image, and (iii) detecting one or more
defects, and identifying one or more types of the one or more
defects based on the one or more extracted features.
2. The method of claim 1, wherein the inspecting step includes:
directing incident light onto and through an opposing edge surface
of at least one of the first and second glass ribbons that is
laterally opposite to the at least one of the first and second edge
surfaces; propagating the light through the at least one of the
first and second glass ribbons, transversely with respect to the
transport direction, such that the light exits through the at least
one of the first and second edge surfaces; and directing an optical
axis of an imaging sensor substantially perpendicularly toward the
at least one of the first and second edge surfaces, to receive the
light exiting the at least one of the first and second edge
surfaces, such that the imaging sensor produces the at least one
image.
3. The method of claim 2, wherein the step of directing the imaging
sensor toward the at least one of the first and second edge
surfaces includes: monitoring a distance from the imaging sensor
and/or a reference position to the at least one of the first and
second edge surfaces as the first and second glass ribbons of the
glass web are moved in the transport direction to the destination;
and automatically adjusting a position of focus of the imaging
sensor as a function of the distance such that the at least one
image remains in focus.
4. The method of claim 1, wherein the step of detecting the one or
more defects, and identifying the one or more types of the one or
more defects, includes: enhancing one or more defect features as
compared to background features in the at least one image; applying
a segmentation process to the enhanced defect features to separate
high contrast features from lower contrast features, thereby
producing a plurality of segments; and extracting features from
each of the plurality of segments by analyzing each segment as to
one or more of the following features: (i) a total area of the
segment, (ii) an eccentricity and/or elongation of the segment,
(iii) a width of the segment, (iv) a height of the segment, and (v)
a fill ratio of the segment.
5. The method of claim 4, further comprising grouping at least some
of the segments together to form at least one aggregated
segment.
6. The method of claim 4, further comprising determining and
identifying the one or more types of the one or more defects based
on the analysis of the segments.
7. The method of claim 6, further comprising making a determination
that one or more of the segments represents a chip when: (i) a
total area of the one or more segments is relatively large, within
a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively low, within a range of relatively low to relatively
high, and (iii) a fill ratio of the one or more segments is
relatively high, within a range of relatively low to relatively
high.
8. The method of claim 6, further comprising making a determination
that one or more of the segments represent a hackle line when: (i)
a width of the one or more segments is relatively small, within a
range of relatively small to relatively large, and (ii) a location
of the one or more segments is relatively close to a periphery of
the edge surface.
9. The method of claim 6, further comprising making a determination
that one or more of the segments represent a Wallner line when: (i)
a total area of the one or more segments is relatively large,
within a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively high, within a range of relatively low to relatively
high, and (iii) a height of the one or more segments is relatively
small, within a range of relatively small to relatively large.
10. The method of claim 6, further comprising making a
determination that one or more of the segments represent an arrest
line when: (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, (ii) an eccentricity and/or elongation of the one or more
segments is relatively high, within a range of relatively low to
relatively high, (iii) a width of the one or more segments is
relatively small, within a range of relatively small to relatively
large, and (iv) a height of the one or more segments is relatively
large, within a range of relatively small to relatively large.
11. The method of claim 1, further comprising automatically
adjusting one or more parameters of the step of cutting the glass
web at the cutting zone based on the detection and identification,
wherein the step of cutting the glass web at the cutting zone
includes heating an elongated zone of the glass web using a laser
delivery apparatus followed by cooling the heated portion of the
glass web to propagate a fracture in a direction opposite to the
transport direction, thereby producing the first and second
ribbons; and the one or more parameters of the step of cutting the
glass web include a power level of incident laser light from the
laser delivery apparatus, and a focus of the incident laser light
from the laser delivery apparatus.
12. An apparatus, comprising: a source apparatus configured to
supply a glass web, the glass web having a length and a width
transverse to the length; a transport mechanism configured to move
the glass web from the source apparatus to a destination in a
transport direction along the length of the glass web; and a
cutting mechanism configured to cut the glass web, at a cutting
zone, along the length into at least first and second glass ribbons
as the glass web is moved in the transport direction from the
source to the destination, such that respective first and second
edge surfaces are produced on the first and second glass ribbons;
and an inspection mechanism configured to optically inspect at
least one of the first and second edge surfaces in real-time as the
first and second glass ribbons of the glass web are moved in the
transport direction to the destination, wherein the inspection
mechanism is configured to execute actions, including: (i) taking
at least one image of the at least one of the first and second edge
surfaces as the first and second glass ribbons are moved in the
transport direction, (ii) extracting one or more features of the at
least one of the first and second edge surfaces from the at least
one image, and (iii) detecting one or more defects, and identifying
one or more types of the one or more defects, based on the one or
more extracted features.
13. The apparatus of claim 12, wherein the inspection mechanism
comprises: a light source configured to direct incident light onto
and through an opposing edge surface of at least one of the first
and second glass ribbons that is laterally opposite to the at least
one of the first and second edge surfaces, such that the light
propagates through the at least one of the first and second glass
ribbons, transversely with respect to the transport direction, such
that the light exits through the at least one of the first and
second edge surfaces; and an imaging sensor comprising an optical
axis directed substantially perpendicularly toward the at least one
of the first and second edge surfaces, and configured to receive
the light exiting the at least one of the first and second edge
surfaces, such that the imaging sensor produces the at least one
image.
14. The apparatus of claim 13, further comprising an automatic
focus mechanism comprising: a distance sensor configured to monitor
a distance from the imaging sensor and/or a reference position to
the at least one of the first and second edge surfaces as the first
and second glass ribbons of the glass web are moved in the
transport direction to the destination; and a motion stage
configured to automatically adjust a position of focus of the
imaging sensor as a function of the varying distance such that the
at least one image remains in focus.
15. The apparatus of claim 12, wherein the inspection mechanism
includes a computer processor configured to operate under the
control of a computer program, which when executed by the computer
processor causes the computer processor to carry out the actions of
detecting the one or more defects, and identifying the one or more
types of the one or more defects, by: enhancing one or more defect
features as compared to background features in the at least one
image; applying a segmentation process to the enhanced defect
features to separate high contrast features from lower contrast
features, thereby producing a plurality of segments; and extracting
features from each of the plurality of segments by analyzing each
segment as to one or more of the following features: (i) a total
area of the segment, (ii) an eccentricity and/or elongation of the
segment, (iii) a width of the segment, (iv) a height of the
segment, and (v) a fill ratio of the segment.
16. The apparatus of claim 15, wherein the inspection mechanism is
further configured to carry out an action of grouping at least some
of the segments together to form at least one aggregated
segment.
17. The apparatus of claim 15, wherein the inspection mechanism is
further configured to carry out an action of determining and
identifying the one or more types of the one or more defects based
on the analysis of the segments.
18. The apparatus of claim 17, wherein the inspection mechanism is
further configured to carry out an action of making a determination
that one or more of the segments represent a chip when: (i) a total
area of the one or more segments is relatively large, within a
range of relatively small to relatively large, (ii) an eccentricity
and/or elongation of the one or more segments is relatively low,
within a range of relatively low to relatively high, and (iii) a
fill ratio of the one or more segments is relatively high, within a
range of relatively low to relatively high.
19. The apparatus of claim 17, wherein the inspection mechanism is
further configured to carry out an action of making a determination
that one or more of the segments represent a hackle when: (i) a
width of the one or more segments is relatively small, within a
range of relatively small to relatively large, and (ii) a location
of the one or more segments is relatively close to a periphery of
the edge surface.
20. The apparatus of claim 17, wherein the inspection mechanism is
further configured to carry out an action of making a determination
that one or more of the segments represent a Wallner line when: (i)
a total area of the one or more segments is relatively large,
within a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively high, within a range of relatively low to relatively
high, and (iii) a height of the one or more segments is relatively
small, within a range of relatively small to relatively large.
21. The apparatus of claim 17, wherein the inspection mechanism is
further configured to carry out an action of making a determination
that one or more of the segments represent an arrest line when: (i)
a total area of the one or more segments is relatively large,
within a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively high, within a range of relatively low to relatively
high, (iii) a width of the one or more segments is relatively
small, within a range of relatively small to relatively large, and
(iv) a height of the one or more segments is relatively large,
within a range of relatively small to relatively large.
22. The apparatus of claim 12, further comprising a feedback
mechanism configured to automatically adjust one or more parameters
of the cutting mechanism and of cutting the glass web at the
cutting zone based on the detection and identification, and wherein
the cutting mechanism includes a laser delivery apparatus
configured to heat an elongated zone of the glass web and a cooling
fluid source configured to cool the heated portion of the glass web
to propagate a fracture in a direction opposite to the transport
direction and cut the glass web, thereby producing the first and
second ribbons; and the one or more parameters of the cutting
mechanism include a power level of incident laser light from the
laser delivery apparatus, and a focus of the incident laser light
from the laser delivery apparatus.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority under 35
U.S.C. .sctn. 119 of U.S. Provisional Application Ser. No.
62/299,750 filed on Feb. 25, 2016, the content of which is relied
upon and incorporated herein by reference in its entirety.
BACKGROUND
[0002] The present disclosure relates to methods and apparatus for
inspecting edge surface quality of a moving web of glass
material.
[0003] Continuous processing of ultra-thin glass web, for example
glass web measuring less than or equal to about 0.3 mm in
thickness, is a relatively new field and presents a number of
manufacturing challenges. A conventional process for producing such
webs includes employing a roll-to-roll technique in which a glass
web is conveyed in a continuous transport between a supply roll and
a take-up roll. To produce final products, for example glass for
flat panel displays or other products, the glass web must be cut or
sliced during the roll-to-roll conveyance of the glass web. A laser
cutting technique (or other suitable cutting technique) may be
employed to slit the glass web to remove bead portions (i.e.,
thickened portions that are located at the peripheral edges of the
glass web that occur when forming the web) during transport. The
glass web may also be cut during roll-to-roll conveyance to achieve
desired width dimensions for later processing.
[0004] The final piece parts delivered to customers often must
exhibit very smooth, particle free edges, with minimal edge defects
and/or edge corner defects. After removal of the beads and/or
cutting the web to width, however, the quality of the edge
surface(s) might not be within tolerances. Conventional approaches
for cutting and inspecting the glass web, however, have not
provided the ability to inspect and evaluate edge surface quality
during the roll-to-roll conveyance of the glass web in a continuous
transport system.
[0005] Accordingly, there are needs in the art for new methods and
apparatus for inspecting edge surface quality of a moving web of
glass material.
SUMMARY
[0006] The present disclosure relates to methods and apparatus for
inspecting edge surface quality of a moving web of glass material,
for example during the removal of beads and/or during the cutting
of the glass web to desired widths.
[0007] Whether a laser cutting technique is employed or some other
cutting technique, edge surface defects generally occur randomly as
they are the result of imperfect process parameters and/or varying
conditions during the cutting and transport process. It is
generally understood that edge surface defect types may be
classified into the following categories: chips (see, FIG. 1),
hackle lines (see, FIG. 2), Wallner lines (see, FIG. 3), and arrest
lines (see FIG. 4). Other categories of edge surface defect types
(which are not illustrated) include frictive damages and
scratches.
[0008] During a roll-to-roll, or continuous transport cutting
process, it would be highly advantageous to be able to perform
real-time edge surface inspection, quantification of edge surface
defects (or quantification of edge surface quality). The prior
state of the art, however, does not permit real-time edge surface
inspection and quantification capabilities. Thus, edge surface
inspection and quality assessments are randomly checked off-line
using suitable techniques, for example by way of automated high
resolution microscope systems, which can generate edge surface
images for specially prepared samples. Such systems, however, have
proven to exhibit very limited speeds and, thus, are used for a
very small number of samples. Moreover, the images produced by the
commercially available high resolution microscope systems must be
interpreted by a trained scientist, which is very tedious,
expensive, and exacerbates an already slow process. Due to the
tedious and excessively slow inspection techniques available to
production personnel, many operators prefer to simply slide their
fingers along the edge surface of a cut glass web to obtain what
limited edge surface quality information may be available from a
tactile inspection.
[0009] Whether a sophisticated high resolution microscope system is
employed or whether a tactile inspection is performed, the results
are either for far fewer than a 100% real-time inspection or are so
crude as to be of questionable value. Consequently, present
production techniques fail to include real-time, systematic and
reliable defect quantification in connection with determining edge
surface quality in continuous conveyance glass web cutting
processes.
[0010] In accordance with one or more embodiments herein, new
methods and apparatus have been developed in which an inline glass
edge inspection system is employed to measure, identify, classify,
and quantify edge surface defects in the glass web in real time.
The inspection system may include back lit illumination of the edge
surface of the glass web, high resolution optical imaging of such
edge surface, mechanically driven in situ auto-focusing, and a
defect classification and quantification algorithm. The defect
classification and quantification algorithm analyzes the brightness
contrast of the edge surface images to identify, classify, and
quantify various defects on or in the edge surfaces.
[0011] Advantages and benefits of one or more embodiments herein
include any of the following:
[0012] Can provide in situ process feedback capability (to change
process parameters) for a glass web roll-to-roll cutting process
involving edge slitting, bead removal, edge grinding, and/or
chamfering.
[0013] Can provide 100% inspection of edge surfaces, which allows
for the capture of transient events, trends, etc.
[0014] Can provide instantaneous feedback to the glass edge shaping
and separation processes, which permits modifications to processing
parameters to improve edge surface quality.
[0015] Can provide a quality control tool to capture statistical
drift of a continuous conveyance process.
[0016] Can provide a non-destructive, non-intrusive, automated edge
surface inspection process, which is tolerant of three dimensional
(3D) glass web motion.
[0017] In accordance with one or more embodiments, methods and
apparatus disclosed herein may provide for: sourcing a glass web,
the glass web having a length and a width transverse to the length;
continuously moving the glass web from the source to a destination
in a transport direction (also known as a down-web direction) along
the length of the glass web; cutting the glass web, at a cutting
zone, along the length into at least first and second glass ribbons
as the glass web is moved in the transport direction from the
source to the destination, such that respective first and second
edge surfaces are produced on the first and second glass ribbons;
and optically inspecting at least one of the first and second edge
surfaces in real-time as the first and second glass ribbons of the
glass web are moved in the transport direction to the
destination.
[0018] In accordance with one or more embodiments the inspecting
operation(s) may include one or more of: (i) taking at least one
image of the at least one of the first and second edge surfaces as
the first and second glass ribbons of the glass web are moved in
the transport direction, (ii) extracting one or more features of
the at least one of the first and second edge surfaces from the at
least one image, and (iii) detecting one or more defects, and
identifying one or more types of the one or more defects, based on
the one or more extracted features.
[0019] The types of the one or more defects may include chips,
hackles, Wallner lines, arrest lines, frictive damage, and
scratches.
[0020] The methods and apparatus may further provide for one or
more of: directing incident light onto and through an opposing edge
of at least one of the first and second glass ribbons that is
laterally opposite to (also known as a cross-web direction from)
the at least one of the first and second edge surfaces; propagating
the light through the at least one of the first and second glass
ribbons, transversely with respect to the transport direction, such
that the light exits through the at least one of the first and
second edge surfaces; and directing an optical axis of an imaging
sensor substantially perpendicularly toward the at least one of the
first and second edge surfaces, to receive the light exiting the at
least one of the first and second edge surfaces, such that the
imaging sensor produces the at least one image.
[0021] Additionally or alternatively, the methods and apparatus may
further provide for one or more of: monitoring a distance from the
imaging sensor (and/or a reference position) to the at least one of
the first and second edge surfaces as the first and second glass
ribbons of the glass web are moved in the transport direction to
the destination; and automatically adjusting a position of focus of
the imaging sensor as a function of the distance such that the at
least one image remains in focus.
[0022] In accordance with one or more embodiments, the methods and
apparatus may further provide for detecting the one or more
defects, and identifying the one or more types of the one or more
defects, to include one or more of: enhancing one or more defect
features as compared to background features in the at least one
image; applying a segmentation process to the enhanced defect
features to separate high contrast features from lower contrast
features, thereby producing a plurality of segments; and extracting
features from each of the plurality of segments by analyzing each
segment as to one or more of the following features: (i) a total
area of the segment, (ii) an eccentricity and/or elongation of the
segment, (iii) a width of the segment, (iv) a height of the
segment, and (v) a fill ratio of the segment.
[0023] Additionally or alternatively, the methods and apparatus may
further provide for determining and identifying the one or more
types of the one or more defects based on the analysis of the
segments.
[0024] For example, a determination that one or more of the
segments represent a chip when one or more of: (i) a total area of
the one or more segments is relatively large, within a range of
relatively small to relatively large, (ii) an eccentricity and/or
elongation of the one or more segments is relatively low, within a
range of relatively low to relatively high, and (iii) a fill ratio
of the one or more segments is relatively high, within a range of
relatively low to relatively high.
[0025] By way of further example, a determination that one or more
of the segments represent a hackle when one or more of: (i) a width
of the one or more segments is relatively small, within a range of
relatively small to relatively large, and (ii) a location of the
one or more segments is relatively close to a periphery of the edge
surface.
[0026] By way of further example, a determination that one or more
of the segments represent a Wallner line when one or more of: (i) a
total area of the one or more segments is relatively large, within
a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively high, within a range of relatively low to relatively
high, and (iii) a height of the one or more segments is relatively
small, within a range of relatively small to relatively large.
[0027] By way of further example, a determination that one or more
of the segments represent an arrest line when one or more of: (i) a
total area of the one or more segments is relatively large, within
a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the one or more segments is
relatively high, within a range of relatively low to relatively
high, (iii) a width of the one or more segments is relatively
small, within a range of relatively small to relatively large, and
(iv) a height of the one or more segments is relatively large,
within a range of relatively small to relatively large.
[0028] Additionally or alternatively, the methods and apparatus may
further provide for automatically adjusting one or more parameters
of cutting the glass web at the cutting zone based on the detection
and identification.
[0029] By way of example, the cutting of the glass web at the
cutting zone may include heating an elongated zone of the glass web
using a laser delivery apparatus followed by cooling the heated
portion of the glass web to propagate a fracture in a direction
opposite to the transport direction, thereby producing the first
and second ribbons. In such an embodiment, the one or more
parameters of the cutting of the glass web may include a power
level of incident laser light from the laser delivery apparatus,
and a focus of the incident laser light from the laser delivery
apparatus.
[0030] Other aspects, features, and advantages will be apparent to
one skilled in the art from the description herein taken in
conjunction with the accompanying drawings.
DESCRIPTION OF THE DRAWINGS
[0031] For the purposes of illustration, there are forms shown in
the drawings that are presently preferred, it being understood,
however, that the embodiments disclosed and described herein are
not limited to the precise arrangements and instrumentalities
shown.
[0032] FIG. 1 is a magnified image of an edge surface of a cut
glass web, where the image includes features indicating that the
edge surface has a defect, for example a chip;
[0033] FIG. 2 is a magnified image of an edge surface of a cut
glass web, where the image includes features indicating that the
edge surface has a defect, for example a hackle;
[0034] FIG. 3 is a magnified image of an edge surface of a cut
glass web, where the image includes features indicating that the
edge surface has a defect, for example a Wallner line;
[0035] FIG. 4 is a magnified image of an edge surface of a cut
glass web, where the image includes features indicating that the
edge surface has a defect, for example an arrest line;
[0036] FIG. 5 is a top schematic view of an apparatus for cutting a
glass web into at least two glass ribbons;
[0037] FIG. 6 is a side, elevational schematic view, which
illustrates further details of the apparatus 100, including a
transport mechanism, a cutting mechanism, and an edge surface
inspection mechanism;
[0038] FIG. 7 is a schematic illustration of an embodiment of the
cutting mechanism of FIG. 6 in which an optical delivery apparatus
and a cooling fluid source operate to propagate a fracture in the
glass web to produce the at least two glass ribbons;
[0039] FIG. 8 is a schematic illustration of an embodiment of the
edge surface inspection mechanism of FIG. 6, including a light
source, an imaging sensor, and an auto-focus mechanism;
[0040] FIG. 9 is a schematic illustration (top view) of a ribbon of
glass that has been cut, thereby producing an edge surface for
inspections, and where the light source is incident on an opposing
edge of the ribbon of glass, propagates through the ribbon of
glass, and exits the edge surface of the ribbon of glass for
detection;
[0041] FIG. 10 is a schematic illustration (side view) of the
ribbon of glass of FIG. 9;
[0042] FIG. 11 is a collection of images representing respective
outputs of an algorithm for optically inspecting an edge surface of
a cut ribbon of glass, detecting one or more defects, and
identifying one or more types of the one or more defects; and
[0043] FIG. 12 is a collection of respective pairs of images, each
pair including an image of an edge surface of a cut ribbon of
glass, and an output image representing a classification of one or
more detected and identified defects.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0044] With reference to the drawings wherein like numerals
indicate like elements there is shown in FIG. 5 a top schematic
view of an apparatus 100 for cutting a glass web 103, for example,
along one or both of the illustrated dashed lines. The illustrated
dashed lines are intended to show cutting lines for removing beads,
however, other and/or additional cutting lines may be employed to
cut the glass web into one or more glass ribbons, for example glass
ribbon 103A.
[0045] FIG. 6 is a side, elevational schematic view, which
illustrates further details of the apparatus 100. In general, the
apparatus 100 operates to source the glass web 103 and continuously
move the glass web 103 from the source 102 to a destination zone in
a transport direction 105 (the down-web direction) along the length
of the glass web 103 (illustrated by the arrows 105). During the
transport of the glass web 103 from the source 102 to the
destination zone, the glass web 103 is cut in a cutting zone 147
into the glass ribbon 103A. (For purposes of clarity, FIG. 6 does
not show the transport path(s) for sections of the glass web 103
created by the removal of the beads.) The glass web 103 has a
length (in the transport direction) and a width transverse to the
length, and the width of the glass ribbon 103A will be smaller than
the overall width of the glass web 103.
[0046] With reference to FIG. 5, the glass web 103 may be provided
by a wide range of sources, for example a down draw glass forming
apparatus (not shown) in which a trough having a forming wedge
permits molten glass to overflow the trough and flow down opposite
sides of the forming wedge, where the respective flows are
subsequently fused together as they are drawn off the forming
wedge. This fusion down drawn process produces a glass web 103,
which may be introduced into the transport mechanisms of the
apparatus 100 for cutting. It is noted that the glass web 103 would
typically include a pair of opposed edge portions 201, 203 and a
central portion 205 spanning between the opposed edge portions 201,
203. Due to the down draw fusion process, the edge portions 201,
203 of the glass ribbon may have corresponding beads of a thickness
that is typically greater than a thickness of the central portion
205 of the glass web 103. The beads may be removed using the
cutting techniques disclosed herein or other more conventional
approaches.
[0047] As will be discussed in greater detail later herein, it is
very desirable that the resulting edge surface(s) of the glass
ribbon 103A (e.g., from removing the bead(s) and/or cutting the
glass web 103 to a specified width) have very high quality, and
that any significant degradation of the quality of the edge
surface(s) should be detected, defects classified, and corrective
actions taken. Therefore, as schematically illustrated in FIG. 6,
the apparatus 100 includes an edge surface inspection mechanism 180
that operates to inspect, detect defects, and identify the type(s)
of such defects in real-time as the glass web 103 is conveyed and
cut.
[0048] The source 102 of glass web 103 may include a spool, onto
which the glass web 103 was first wound, e.g., following a fusion
down draw process. Typically, the spool used as source 102 would be
provided with a relatively large diameter to present a relatively
low bending stress to accommodate the characteristics of the glass
web 103. Once coiled onto the spool used as source 102, the glass
web 103 may be uncoiled from that spool and introduced into the
transport mechanisms of the apparatus 100.
[0049] The destination zone of the apparatus 100 may include any
suitable mechanisms for accumulating the glass ribbon 103A (and
waste beads, not shown). In the example illustrated in FIG. 6, the
destination zone includes a spool 104 for receiving and winding the
glass ribbon 103A. The spool 104 should be provided with a
relatively large diameter to present a suitable bend radius in
order to accommodate the characteristics of the glass ribbon
103A.
[0050] The apparatus 100 includes a transport mechanism having a
number of individual elements that cooperate to continuously move
the glass web 103 from the source 102, for example a spool of wound
glass, to the destination spool 104 in the transport direction.
This transport function may be accomplished without degrading the
desirable characteristics of the ribbon edge surfaces, as produced
from the cutting operation, or either (pristine) major surface of
the central portion 205 of the glass web 103 and/or ribbon 103A. In
short, the transport function is accomplished without degrading
desirable characteristics of the glass ribbon 103A.
[0051] By way of example, the apparatus 100 may include a plurality
of noncontact support members 106, 108, rollers, etc., to guide the
glass web 103 and glass ribbon 103A through the system from the
source 102 to the destination spool 104. The non-contact support
members 106, 108 may be flat and/or curved to achieve desirable
directional conveyance of the respective work pieces. Each of the
noncontact support members 106, 108 may include a fluid bar and/or
a low friction surface to ensure the glass web 103 and glass ribbon
103A are suitably conveyed through the system without damage or
contamination. When a given non-contact support member 106, 108
includes an fluid bar, such element includes a plurality of
passages and ports configured to provide a positive fluid pressure
stream (for example air), and/or a plurality of passages and ports
configured to provide a negative fluid pressure stream, to the
associated surface of the glass web 103 and/or glass ribbon 103A to
create an air cushion for such noncontact support. A combination of
positive and negative fluid pressure streams may stabilize the
glass web 103 and glass ribbon 103A during transport through the
system.
[0052] Optionally, a number of lateral guides (not shown) may be
employed proximate to the edge portions 201, 203 of the glass web
103 and/or the edge surfaces of the glass ribbon 103A to assist in
orienting the glass web 103 and/or glass ribbon 103A in a desired
lateral position relative to the transport direction. For example,
the lateral guides may be implemented using rollers that engage a
corresponding one of the opposed edge portions 201, 203 of the
glass web 103, and/or one or more edge surfaces of the glass ribbon
103A. Corresponding forces applied to the edge portions 201, 203 by
the corresponding lateral guides may shift and align the glass web
103 in the proper lateral orientation as the glass web 103 is
conveyed through the apparatus.
[0053] Due to its high modulus, notch sensitivity and brittleness,
however, it is beneficial for the glass web 103 to have very
consistent and symmetrical stress and strain fields in the cutting
zone 147 in order to exhibit suitable edge characteristics (minimal
fractures) after cutting. Therefore, the apparatus 100 includes a
tensioning mechanism 130 (for example, as would be understood by
one of ordinary skill in the art, a dancer, a web accumulator, a
roller with a break) operating to provide consistent and symmetric
stress fields and strain fields in the cutting zone 147. In
accordance with one or more embodiments herein, tensioning is
carefully and independently (from edge portions 201, 203)
controlled in the glass ribbon 103A in order to achieve consistent
and symmetric stress and strain fields. This approach is intended
to result in a very fine, particle free edge surfaces that
minimizes edge and/or edge corner defects.
[0054] In order to achieve the aforementioned tensioning
functionality, the tensioning mechanism 130 monitors the tension,
determines whether the tension is within prescribed limits, and
varies the force based on the determination to ensure the tension
is within prescribed limits. As schematically illustrated in FIG. 6
via dashed lines, the tensioning mechanism 130 includes one or more
means for sensing the tension in the glass ribbon 103A and a means
for changing such tension if such tension is outside a prescribed
range.
[0055] The apparatus 100 further includes a cutting mechanism 120
that cuts or severs the glass web 103 in the cutting zone 147 as
the glass web 103 passes over, for example, the noncontact support
member 108. As will be described in more detail later herein, the
cutting mechanism 120 may make a single cut or may simultaneously
make multiple cuts; however, a significant characteristic of the
cutting process is that the resultant glass ribbon 103A (and/or
further numbers of ribbons) will exhibit edge surface(s) that are
subject to defects, for example chips, hackles, Wallner lines,
arrest lines, frictive damage, and scratches.
[0056] With reference to FIG. 7, in one or more embodiments, the
cutting mechanism 120 may include an optical delivery apparatus for
heating an elongated zone of the glass web 103, and a cooling fluid
source that applies coolant to the heated portion of the glass web
103 to propagate a fracture in a direction opposite to the
transport direction, thereby producing the glass ribbons 103A. In
accordance with one or more embodiments, the cutting mechanism 120
may include multiple heating/cooling apparatus arranged across the
glass web 103 to produce multiple simultaneous cuts. The positions
of the respective heating/cooling apparatus are adjustable in order
to meet particular customer requirements as to the width of the
glass ribbon 103A.
[0057] The optical delivery apparatus may include a radiation
source, for example a laser, although other radiation sources may
be employed. The optical delivery apparatus may further include
other elements to shape, adjust direction and/or adjust the
intensity of an optical beam, for example one or more polarizers,
beam expanders, beam shaping apparatus, etc. Preferably, the
optical delivery apparatus produces a laser beam 169 having a
wavelength, power, and shape suitable for heating the glass web 103
at a location on which the laser beam 169 is incident.
[0058] It has been found that a laser beam 169 of significantly
elongated shape works well. The boundary of the elliptical
footprint of the laser beam 169 may be determined as the point at
which the beam intensity has been reduced to 1/e.sup.2 of its peak
value. The elliptical footprint may be defined by a major axis that
is substantially longer than a minor axis. In some embodiments, for
example, the major axis may be at least about ten times longer than
the minor axis. However, the length and width of an elongated
radiation heated zone 227 are dependent upon the desired severing
speed, desired initial crack size, thickness of the glass ribbon,
laser power, etc., and the length and width of the radiation zone
may be varied to suit the particular cutting conditions.
[0059] The cooling fluid source 181 operates to cool the heated
portion of glass web 103 by application of cooling fluid,
preferably a jet of fluid, for example though a nozzle or the like.
The geometry of the nozzle, etc., may be varied to suit the
particular process conditions. The cooling fluid may include water,
however, any other suitable cooling fluid or mixture may be
employed that does not damage the glass web 103. The cooling fluid
may be delivered to the surface of the glass web 103 to form a
cooling zone 319, where the cooling zone 319 may trail behind the
elongated radiation zone 227 to propagate a fracture (initiated by
an induced crack). The combination of heating and cooling
effectively severs the glass web 103 to create the glass ribbon
103A, while minimizing or eliminating undesired residual stress,
micro-cracks or other irregularities in edge surface(s) created by
the cut, which result in the aforementioned defects.
[0060] As noted above, the apparatus 100 includes the inspection
mechanism 180 to address the problem of such defects in the edge
surface(s) or the glass ribbon 103A. The inspection mechanism 180
optically inspects one or more edge surfaces of the glass ribbon
103A (and/or further glass ribbons) as the glass web 103 is moved
in the transport direction 105 to the destination. From a
functional point of view, the inspection mechanism 180 executes
actions, including: (i) taking at least one image of the edge
surface(s) as the glass ribbon 103A is moved in the transport
direction 105, (ii) extracting one or more features of the edge
surface(s) from the at least one image, and (iii) detecting one or
more defects, and identifying one or more types of the one or more
defects, based on the one or more extracted features.
[0061] With reference to FIGS. 6 and 8, one or more embodiments of
the inspection mechanism 180 may include one or more light sources
182, one or more imaging sensors 184, one or more auto-focus
mechanisms 186, one or more motion sensors 188, and a processing
and control unit 190. For purposes of clarity and brevity, the
embodiments below are shown and described as inspecting one of the
edge surfaces of the glass ribbon 103A (resulting from a cut). One
skilled in the art, however, will appreciate that the inspection
mechanism 180 may be modified to inspect more than one edge surface
simply by incorporating multiple light sources 182, imaging sensors
184, auto-focus mechanisms 186, motion sensors 188, and/or
processing and control units 190 in order to evaluate such edge
surfaces in real time.
[0062] The light source 182 directs incident light onto and through
a proximal edge surface of the glass ribbon 103A, which is an
opposing edge of the glass ribbon 103A that is laterally opposite
to (cross-web from) the edge surface that is being inspected (a
distal edge surface). As illustrated in FIGS. 9 and 10, incident
light from the light source 182 propagates through the glass ribbon
103A, transversely with respect to the transport direction, such
that the light exits through the edge surface being inspected.
Uniform illumination of the proximal edge surface of the glass
ribbon 103A is very desirable in detecting micro scale defect
features on the edge surface being inspected. In addition, it is
also desirable for the imaging sensor 184 and processing (discussed
later) to be robust enough to tolerate some level of vertical
vibration of the glass ribbon 103A during the inspection process.
Therefore to provide uniform illumination and to tolerate vertical
vibration, one or more embodiments may employ a light source 182
that includes a bright field (transmission) configuration using a
line shaped light emitting diode (LED) light source (see, FIGS.
9-10). In this configuration, strong waveguide action (see FIG. 10)
allows light to travel through the glass ribbon 103A quite
efficiently and hence provide bright and uniform illumination of
the edge surface being inspected. Furthermore, the waveguide action
of the glass ribbon on the incident light produces an illumination
of the edge surface being inspected that is less sensitive to
vertical vibrations of the glass ribbon 103A.
[0063] In one or more embodiments, it is considered very desirable
to generate a "brightfield" image of the defects on and/or in the
edge surface of the glass ribbon 103A. Fine grayscale features
present under brightfield illumination enable more accurate sizing
and classification of edge features than high-contrast geometries.
Notably, however, "darkfield" geometries can be very useful and
effective when sensitive detection of features is desired, for
instance when using low optical magnification optics to detect
particles or glass chips. In both cases, care should be taken to
maximize the dynamic range of features on the resulting images,
including preventing saturation and/or bottoming out of the
features, whereby the details of the defect features are lost
within poor contrast. It has been found that good results may be
obtained when the imaging sensor 184 has 8-bits of grayscale and
sufficient lighting is provided to interact with the defect
features; indeed, such a combination has been found to produce a
high contrast image. Based on use of the aforementioned high
brightness LED and efficient lighting geometry (due to the
waveguide approach), it may be a relatively simple matter to
position the LED to within a few centimeters of the proximal edge
surface of the glass ribbon 103A. Nevertheless, light diverges
heavily from a bare LED. Thus, one may employ one or more
additional optical elements between the light source (whether an
LED, halogen lamp, laser, or any other illuminator) and the
proximal edge surface of the glass ribbon 103A to increase coupling
efficiency or otherwise condition the lighting. For instance, one
may employ a condensing lens to collect more light coming from the
light source and focus the light to a point at or near the proximal
edge surface of the glass ribbon 103A. This may greatly increase
brightness. Additionally and/or alternatively, one may employ a
diffuser between the light source and the proximal edge surface of
the glass ribbon 103A to even the output spatially. Still further,
a color filter and/or a polarizer may be employed to affect the
light reaching the imaging sensor 184 to target some property of
the defects in question.
[0064] To obtain coupling of the light from the light source into
the proximal edge of the glass ribbon 103A (To achieve waveguiding)
it is desirable to have a suitable edge finish (for example a
straight, mirror-like finish attained via the aforementioned laser
cut). In contrast, when the proximal edge of the glass ribbon 103A
exhibits the characteristics of a ground edge, an attempt to couple
light from the light source into the proximal edge of the glass
ribbon 103A exhibits high loss because the edge scatters a lot of
light. To compensate, one may increase the intensity of the light
source to provide sufficient illumination for adequate defect
contrast, or instead use a reflection geometry.
[0065] The imaging sensor 184 is preferably provided such that an
optical axis thereof is directed substantially perpendicularly
toward the edge surface being inspected. The imaging sensor 184
receives the light exiting the edge surface being inspected and
produces at least one image of the edge surface. By way of example,
the imaging sensor 184 is preferably a high resolution image
acquisition device, with sufficient speed to be effective despite a
conveyance speed of the glass ribbon 103A in the range of about 200
mm/s or higher, and a relatively large field of view (with
reference to the edge surface dimensions). To achieve a high
resolving power, the imaging sensor 184 may employ a high numerical
aperture (NA) lens (e.g., above 0.2) and a light sensitive sensor
(e.g., a charge couple device (CCD) array, for example a CCD having
7 um per pixel resolution). High NA optics are beneficial for
highlighting fine defect features on the edge surface (for example
hackle lines). High NA optics also are characterized by small
depths-of-field (DOF), which may be less than about 50 .mu.m.
Consequently, as discussed below, one should very carefully
measure, track, and adjust for any change in distance from the
imaging sensor 184 to the edge surface to remain within the
aforementioned DOF.
[0066] With reference to FIG. 8, to accommodate practical
environments in which a distance, D, from the edge surface being
inspected to the imaging sensor 184 is susceptible to variation as
the glass ribbon 103A moves through the system, the inspection
mechanism 180 may include the automatic focus mechanism 186. By way
of example, the automatic focus mechanism 186 may include a
distance sensor that monitors the distance from the imaging sensor
184 (and/or some reference position) to the edge surface being
inspected as the glass ribbon 103A moves in the transport direction
to the destination. The automatic focus mechanism 186 automatically
adjusts a position of focus of the imaging sensor 184 as a function
of the distance D such that the at least one image remains in
focus. By way of example, the automatic focus mechanism 186 may
include a motion stage that automatically adjusts a position of the
imaging sensor 184 relative to the edge surface as a function of
the distance D to adjust the aforementioned position of focus
(i.e., to maintain a constant distance D) such that the at least
one image remains in focus.
[0067] Alternatively and/or additionally, the automatic focus
mechanism 186 may interface with an adjustable lens system of the
imaging sensor 184 to automatically adjust the lens system as a
function of a varying distance D to adjust the aforementioned focal
length. When the imaging sensor 184 includes such an adjustable
lens system, the optics of the lens itself may be adjusted to vary
a focal length of the lens, and therefore the motion stage (and the
resultant translational movement of the imaging sensor 184 towards
and/or away from the edge surface of the glass ribbon 103A) may be
avoided. Indeed, the position of the imaging sensor 184 may remain
fixed. Nevertheless, adjustments in the focal length of the imaging
sensor 184 may be made to account for variations in the distance D
from the imaging sensor 184 to the edge surface of the glass ribbon
103A during conveyance thereof.
[0068] Examples of the images of the edge surface being inspected
have been presented above (FIGS. 1-4), which may include such
defects as chips, hackles, Wallner lines, arrest lines, frictive
damage, and scratches. To detect and identify the type(s) of
defects that may be present in the one or more images of the edge
surface being inspected, the inspection mechanism 180 may include
the processing and control unit 190, which carries out an algorithm
for analyzing the features of the one or more images.
[0069] By way of example, the processing and control unit 190 may
include a computer processor operating under the control of a
computer program, which may be stored in a digital storage medium.
When the computer program is executed by the computer processor,
the computer program causes the computer processor to carry out the
actions of detecting the one or more defects, and identifying the
one or more types of the one or more defects. More specifically,
the algorithm may include one or more of: (i) enhancing one or more
defect features as compared to background features in the at least
one image; (ii) applying a segmentation process to the enhanced
defect features to separate high contrast features from lower
contrast features, thereby producing a plurality of segments; and
(iii) extracting features from each of the plurality of segments by
analyzing each segment as to one or more predetermined features.
Such extracted features may include: (i) a total area of the
segment, (ii) an eccentricity and/or elongation of the segment,
(iii) a width of the segment, (iv) a height of the segment, and (v)
a fill ratio of the segment.
[0070] Further details as to how to enhance one or more defect
features, apply a segmentation process, and extract features from
each of the plurality of segments will be provided later herein. At
present, however, it has been discovered that the above-noted
features of the segments may be used to detect and identify types
of defects.
[0071] For example, the processing and control unit 190 of the
inspection mechanism 180 employs the algorithm to make a
determination that one or more of the segments represent a chip
when one or more of: (i) a total area of the one or more segments
is relatively large, within a range of relatively small to
relatively large, (ii) an eccentricity and/or elongation of the one
or more segments is relatively low, within a range of relatively
low to relatively high, and (iii) a fill ratio of the one or more
segments is relatively high, within a range of relatively low to
relatively high.
[0072] Additionally or alternatively, the processing and control
unit 190 of the inspection mechanism 180 may employ the algorithm
to make a determination that one or more of the segments represent
a hackle when one or more of: (i) a width of the one or more
segments is relatively small, within a range of relatively small to
relatively large, and (ii) a location of the one or more segments
is relatively close to a periphery of the edge surface.
[0073] Additionally or alternatively, the processing and control
unit 190 of the inspection mechanism 180 may employ the algorithm
to make a determination that one or more of the segments represent
a Wallner line when one or more of: (i) a total area of the one or
more segments is relatively large, within a range of relatively
small to relatively large, (ii) an eccentricity and/or elongation
of the one or more segments is relatively high, within a range of
relatively low to relatively high, and (iii) a height of the one or
more segments is relatively small, within a range of relatively
small to relatively large.
[0074] Additionally or alternatively, the processing and control
unit 190 of the inspection mechanism 180 may employ the algorithm
to make a determination that one or more of the segments represent
an arrest line when one or more of: (i) a total area of the one or
more segments is relatively large, within a range of relatively
small to relatively large, (ii) an eccentricity and/or elongation
of the one or more segments is relatively high, within a range of
relatively low to relatively high, (iii) a width of the one or more
segments is relatively small, within a range of relatively small to
relatively large, and (iv) a height of the one or more segments is
relatively large, within a range of relatively small to relatively
large.
[0075] With reference to FIG. 6, the processing and control unit
190 of the inspection mechanism 180 may provide a feedback
mechanism that automatically adjusts one or more parameters of the
cutting mechanism 120, the cutting mechanism cutting the glass web
103 at the cutting zone based on the detection and identification
of defects. For example, in embodiments where the cutting mechanism
120 includes a laser delivery apparatus, the one or more parameters
of the cutting mechanism 120 may include a power level of incident
laser light from the laser delivery apparatus, and/or a focus of
the incident laser light 181 from the laser delivery apparatus.
[0076] Turning now to further details concerning how the algorithm
within the processing and control unit 190 determines the existence
of and types of defects on the edge surface being inspected, a
number of features of various types of defects will be
discussed.
[0077] With reference to FIG. 1, a chip is discernible as a defect
chipped off a portion of the edge surface due to concentrated
loads. Chips can be recognized in the image by a human visual
inspection; however, a chip can be made of several clustered blobs,
which presents a challenge to detection and identification using a
machine algorithm. A simple form of an oval-shaped chip is
generally composed of a bright region and a dark region resulting
from the aforementioned illumination. In practical terms, several
chips are often located together, which produces a number of blobs
in the image of the edge surface being inspected. In general,
features of a chip typically include: strong contrast, large size
(height, width and/or area), and low eccentricity.
[0078] With reference to FIG. 2, hackle lines are defects that
separate portions of the crack surface, each of which has rotated
from the original crack plane in response to a lateral rotation or
twist in the axis of principal tension. Hackle lines generally
appear in a group, starting from a periphery of the edge surfaces
and propagating within the glass ribbon 103A. Hackle lines are very
thin structures that pose a challenge for the inspection mechanism
180. In general, features of a hackle line typically include:
strong contrast, small size (height, width and/or area), starts
from a periphery of the edge of the glass ribbon 103A, very thin
(small width versus large height), lines being spatially segregated
from one another.
[0079] With reference to FIG. 3, a Wallner line is a rib-shaped
mark with a wavelike contour caused by a temporary excursion of the
crack front out of plane in response to a tilt in the axis of
principal tension. A Wallner line may also form from passage of the
crack front through a region with a locally shifted stress field,
as at an inclusion, pore, or surface discontinuity. In optical
images taken of the edge surface, Wallner lines appear to have
lower contrast and resemble line-like structures. According to
current understandings, Wallner lines are indications of the
existence of vibration or mechanical shock in the manufacturing
process as the glass web 103 moves in the transport direction. In
general, features of Wallner lines typically include: weak
contrast, large size (height, width and/or area), and low
eccentricity.
[0080] With reference to FIG. 4, an arrest line is a sharp line on
the edge surface defining the crack front shape of an arrested or
momentarily hesitated crack prior to resumption of crack
propagation under a more or less altered stress configuration. An
arrest line generally represents a stress change across the glass
web 103, which may reduce the overall strength. An arrest line
spans across the edge surface extending to the two major surfaces
of the glass ribbon 103A, and has a line shape structure, which
manifests as a high eccentricity in the image of the edge surface.
In addition, a strong contrast of intensity resides across the
arrest line in the image. These features are used in the algorithm
to differentiate arrest lines from other types of defects. In
general, features of an arrest line typically include: strong
contrast, large size (height, width and/or area), high
eccentricity, and extension across most if not all of the edge
surface of the glass ribbon 103A.
[0081] The one or more portions of the algorithm that identify the
types of defects within the one or more images of the edge surface
generally include four modules; namely, a feature enhancement
module, a segmentation module, a feature extraction and
classification module, and a grouping module. FIG. 11 is a
collection of images representing respective outputs of the
above-noted modules. In summary, the modules operate as follows.
The feature enhancement module receives the one or more images of
the edge surface and highlights defect regions of interests within
the image so the segmentation module is more successful in
employing a threshold technique to isolate each defect. A set of
features are extracted from the isolated defects and fed into a
classifier process to produce a blob-level classification. Next,
the defects are grouped into logically meaningful (related) blobs.
Finally, certain features are extracted from the grouped blobs and
are used as final output for decision making as to the type(s) of
defects existing on/in the edge surface.
[0082] The feature enhancement module serves to highlight the
defect regions and suppress background signals within the image of
the edge surface (see image 50 in FIG. 11). Indeed, the image of
the edge surface is likely to include some illumination
non-uniformities due to variations in the lighting and diffraction
on the edge surfaces of the glass ribbon 103A. The feature
enhancement module is intended to perform a global threshold to map
out the defect regions on the image by performing a local threshold
to produce an enhanced difference image (see image 52 in FIG. 11).
As noted above, the enhanced difference image results in better
image segmentation performance (as discussed in more detail later
herein).
[0083] The features of the defect(s) within the image of the edge
surface are represented by intensity changes and may be enhanced,
for example, using a difference filter to implements the
aforementioned local threshold. One example of such a difference
filter is a two scale Haar wavelet filter, which enhances features
under different scales. The Haar filter may be represented by the
following function:
.psi. a ( x ) = { 1 / d 0 .ltoreq. x < d - 1 / d - d < x <
0 0 elsewhere ##EQU00001##
[0084] where it may be assumed that the glass ribbon 103A is being
transported along an X-axis, and the variable d corresponds to a
scale of interest. A smaller d may enhance fine scale features, for
example hackle lines, while a larger d may enhance other defect
features (as well as suppressing noise).
[0085] An example of a Haar filtered image (see image 52 in FIG.
11) has d=1, which boosts features of hackle lines at the bottom
left of the image of the edge surface (compare with image 50 in
FIG. 11). Another example of a Haar filtered image (see image 54 in
FIG. 11) has d=3, which boosts features of a chip.
[0086] The segmentation module receives the enhanced difference
image (filtered image) from the feature enhancement module and
separates features of defects that appear to be connected in the
enhanced difference image. For example, features of Wallner lines
may appear to connect with features of hackle lines. As mentioned
above, different defect types exhibit features having different
contrast intensities. For example, Wallner lines may have very
smooth intensity variation while other defects, for example arrest
lines or chips, may have very strong intensity changes. On the
other hand, the features of different defects may appear to be
connected, for example the features of Wallner lines and the
features of hackle lines (see, upper left and lower left portions
of image 50 in FIG. 11). The segmentation module separates these
features such that the different defects may be properly
categorized.
[0087] By way of example, a dual threshold segmentation technique
may be employed to implement the segmentation module, for example
using modified hysteresis thresholding. Hysteresis thresholding is
a technique to first identify high response pixels, and then
recursively connecting adjacent pixels that are above a lower
response threshold. In one or more embodiments, hysteresis
thresholding may be applied along an X-axis (left-to-right in FIG.
11) of the enhanced difference image, but not along the Y-axis (up
and down in FIG. 11), to segment the high response segments without
joining weak intensity defects. The use of hysteresis thresholding
may thus avoid unwarranted connections between different defects
along the Y-axis, produce meaningful segmentation, and reduce
clutter along the X-axis. By way of example, the result of
segmentation of the Haar filtered image (see, image 54 of FIG. 11)
is shown as image 56 in FIG. 11, where different shading (which
represent respective colors in a color image) indicates different
segments.
[0088] The feature extraction and classification module detects and
extracts features (called blob features or simply blobs, which are
the features of each Binarized Linear Object (BLOB) from each of
the segments, which are then used to classify the subject features
into types of defects. By way of example, the feature extraction
and classification module may be implemented via a binary decision
tree classification technique, which permits artisans to observe,
test and select key features for satisfactory classification
results. The classifier algorithm may be a straight rule-based
classification, a neural network, an m-of-n classifier, etc. In
this regard, a set of standard and non-standard blob features may
be used. The standard features may include one or more of: area,
bounding box, eccentricity, orientation, centroid, and/or fill rate
(which is the area/convex Area). The non-standard blob features may
include one or more of: averaged horizontal integration, effective
sub-blob count, effective width, and/or effective degree of
elongation. Notably, the non-standard blob features were found to
produce better classification results.
[0089] The averaged horizontal integration may be defined as the
column average of summation along each row. The purpose of the
averaged horizontal integration is to reflect overall intensity
changes along the X-axis. It has been found to be useful in
identifying arrest lines, especially when the intensity change
along the X-axis is small but the overall intensity change is
strong.
[0090] The effective sub-blob may be employed to calculate the
actual number of blobs when tightly packed thin lines are
recognized as one blob after the threshold process is employed.
Calculating the effective number of sub-blobs has been found to be
very useful in identifying hackle lines without having to find a
way to split the blob into sub-blobs. The technique operates to
count the number of false pixels (black) blocks along each row
within the blob. The average number of black blocks for all the
rows strongly relates to the number of sub-blobs in a super blob.
For example, in a segment having three tightly packed blobs (e.g.,
semi-vertical "white" lines), most rows would have three white
blocks separated by two black blocks, indicating the super blob
appears as an aggregate of three sub-blobs. The effective width of
the sub-blobs may be calculated as the average width (average of
row summation, assuming the primary orientation of the defect is
vertical, extending along the Y-axis) divided by the effective
sub-blob count. The effective width may be a useful feature in
identifying hackle lines, since the thickness of a hackle line is
very small.
[0091] The Effective Degree of Elongation may be considered to be
similar to eccentricity. Eccentricity for an ellipse is defined
as:
e = 1 - b 2 a 2 ##EQU00002##
where a is the major axis length, b is the minor axis length, and a
and b are obtained by fitting an ellipse to the blob. Conveniently,
one may use the ratio of b to a (width/height ratio) to indicate
the eccentricity of the blob. The eccentricity may be useful in
identifying elongated structures; however, eccentricity might not
be accurate enough to represent the degree of elongation for
rib-shaped features, which might be better represented by a
length/thickness ratio. It has been found that the more curved the
structure is, the lower the eccentricity.
[0092] Additionally and/or alternatively, one may define an
effective degree of elongation as:
e ' = 1 - A 2 a 4 ##EQU00003##
where A is the area of the blob, and the minor axis length b of the
eccentricity formula is replaced with A/a. The effective degree of
elongation has been found to work better for rib shaped structures,
although it is not an exact representation of the length/thickness
ratio.
[0093] A plurality of the extracted blob features are used for blob
level classification (see, image 58 in FIG. 11). By way of example,
the following list may be employed to summarize the processes and
features used for classification of the blobs and determinations of
defect types.
[0094] For detecting and classifying a defect as a chip, it has
been found that suitable results may be obtained by subjecting the
image of the edge surface to a Haar (scale 3) filter, applying
hysteresis thresholding to produce segments, and identifying blobs
as those with relatively high averaged horizontal integration
values. With such processing, the resultant extracted blob features
would suggest a chip when one or more of: (i) a total area of a
blob is relatively large, within a range of relatively small to
relatively large, (ii) an eccentricity and/or elongation of a blob
is relatively low, within a range of relatively low to relatively
high, and (iii) a fill ratio of a blob is relatively high, within a
range of relatively low to relatively high.
[0095] For detecting and classifying a defect as one or more hackle
lines, it has been found that suitable results may be obtained by
subjecting the image of the edge surface to a Haar (scale 1)
filter, and applying a single lower-level threshold to produce
segments. With such processing, the resultant extracted blob
features would suggest one or more hackle lines when one or more
of: (i) an effective width of the blob(s) is/are relatively small,
within a range of relatively small to relatively large, and (ii) a
location of the blob(s) is/are relatively close to a periphery of
the edge surface of the glass ribbon 103A.
[0096] For detecting and classifying a defect as one or more
Wallner lines, it has been found that suitable results may be
obtained by subjecting the image of the edge surface to a Haar
(scale 3) filter, and applying a single lower-level threshold to
produce segments. With such processing, the resultant extracted
blob features would suggest one or more Wallner lines when one or
more of: (i) a total size (e.g., height, width and/or area) of the
blob(s) is/are relatively large, within a range of relatively small
to relatively large, and (ii) an eccentricity and/or elongation of
the blob(s) is/are relatively high, within a range of relatively
low to relatively high.
[0097] Additionally and/or alternatively, for detecting and
classifying a defect as one or more Wallner lines, it has also been
found that suitable results may be obtained by subjecting the image
of the edge surface to a Haar (scale 3) filter, and applying an
hysteresis threshold to produce segments. With such processing, the
resultant extracted blob features would suggest one or more one or
more Wallner lines when one or more of: (i) a total size (e.g.,
height, width and/or area) of the blob(s) is/are relatively large,
within a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the blob(s) is/are relatively
high, within a range of relatively low to relatively high, and
(iii) a height of the blob(s) is/are relatively small, within a
range of relatively small to relatively large.
[0098] For detecting and classifying a defect as one or more arrest
lines, it has been found that suitable results may be obtained by
subjecting the image of the edge surface to a Haar (scale 3)
filter, applying hysteresis thresholding to produce segments, and
identifying blobs as those with relatively high averaged horizontal
integration values. With such processing, the resultant extracted
blob features would suggest one or more arrest lines when one or
more of: (i) a total area of the blob(s) is/are relatively large,
within a range of relatively small to relatively large, (ii) an
eccentricity and/or elongation of the blob(s) is/are relatively
high, within a range of relatively low to relatively high, (iii) a
width of the blob(s) is/are relatively small, within a range of
relatively small to relatively large, and (iv) a height of the
blob(s) is/are relatively large, within a range of relatively small
to relatively large (for example a total height across 90% of the
edge surface).
[0099] The above-noted features have been found to provide a
relatively high level of separation between different defects and
to perform fairly well at the blob-level. Notably, the hackle lines
may produce high responses in both scale 1 and scale 3 Haar
filtered images; however, the scale 3 filtered images have been
found to possibly obscure the thin line-like features of hackle
lines, and thus may result in a false classification as chips.
[0100] By way of example, reference is made to image 58 of FIG. 11,
which illustrates the classification of the defects by type (chip,
hackle lines, Wallner lines, and arrest lines) using shading
(and/or color). The classification of defects in image 58 of FIG.
11 is shown by way of greyscale; however, during experimentation,
the laboratory system produced an indication of the classification
of defects by way of color. In any case, the greyscale key at the
bottom of FIG. 11 is intended to show: minor defects using
greyscale 60, unknown defects using greyscale 62, bright features
using greyscale 64, dark features using greyscale 66, Wallner lines
using greyscale 68, hackle lines using greyscale 70, arrest lines
using greyscale 72, and chip defects using greyscale 72. Inasmuch
as the resolution of the greyscale of FIG. 11 may be lower than
optimum, one can envision that a color representation of the
defects may be: minor defects using dark blue 60, unknown defects
using medium blue 62, bright features using light blue 64, dark
features using light green 66, Wallner lines using yellow 68,
hackle lines using orange 70, arrest lines using bright red 72, and
chip defects using brown 72. Irrespective of whether color or
greyscale is employed, the defects of image 58 in FIG. 11 include
chips, Wallner lines, and hackle lines.
[0101] In some cases, due to web motion or certain glass surface
defects, portions of the edge surface of the glass ribbon 103A may
not be well captured within the image. For example, a dark area on
the edge surface may correspond to an out-of-focus region, where
not enough photons are captured by the imaging sensor 184, while an
overly bright region may suggest an inclined mirror surface. It is
highly unlikely that some form of image processing will recover
features in such areas, and thus it may be advantageous to map out
these areas for future analysis. The algorithm herein may include a
module to map out these areas using a global thresholding
technique. It is desirable, however, that the module avoid the
inclusion of background regions in the image because background
regions also appear as dark regions. This may be difficult, since
most defects exist on the periphery of the edge surfaces, thereby
creating a very uneven surface for foreground extraction. In
addition, diffraction on a corner of the edge surface and/or a flat
surface may obscure the true boundary of the edge surface in the
image. Furthermore, the edge surface may not be exactly horizontal,
due slight distortions of the glass ribbon 103A or stress therein.
To compensate for these effects, the algorithm herein introduces a
new way to extract the sample area from the edge surface images
using a user-defined thickness. It is assumed that the sample area
of the edge surface is horizontally positioned within a small
window, and thus one may implement a box matched filter, the height
of which is the same as the given sample area thickness. The
filtered image will have a local maximum along the Y-axis at the
center of the sample area. With the calculated sample area center
and user-defined thickness, the sample area may be successfully
extracted. A global thresholding may then be carried out on the
masked sample area to avoid inclusion of the background.
[0102] The grouping module may be employed to address the
possibility that the segmentation module produces over-segmented
blobs, especially for chips. After blob-level classification, it
may be desirable to merge spatially close blobs to form logically
more accurate representations of defects. Grouping of blobs is
mostly used for hackle lines and chips (see, image 60 in FIG.
11).
[0103] For hackle lines, the algorithm herein provides for grouping
and performing another level of classification, since one of the
features of hackle lines is that they are spatially segregated. The
grouping of hackle lines may be performed by iteratively looking at
adjacent regions for each hackle line, which is based on the
assumption that hackle lines appear segregated. Next, thresholds of
width and height may be used to eliminate small segregations of
hackle lines.
[0104] For chips, the algorithm herein also provides for grouping
and performing another level of classification. The simplest form
of a chip may include two blobs, each representing a "mirror" face
and a "dark" face of a chip. In a real case, chips often appear in
groups. It may be desirable to show a group of closely packed chips
as one chip. In addition, certain adjacent defects, which are too
small to be determined or classified under blob-level
classification, may also be grouped with a chip to achieve a
complete and logical segmentation. One issue in grouping is to
determine whether a non-chip defect blob should be connected to a
chip blob. Although possible, it is desirable not to use only
spatial closeness as the rule of the grouping determination. Thus,
the algorithm herein may operate on the assumption that a non-chip
blob is connected to a chip blob when a predefined portion of the
perimeter of the non-chip blob resides in the dilated chip blob.
While other ways to define connectivity are also possible, it has
been found that the foregoing approach is suitable for most cases.
It may be desirable to iteratively connect undetermined areas,
Wallner lines, and bright regions to form logically correct
segmentation.
[0105] FIG. 12 is a collection of respective pairs of images, each
pair including an image of an edge surface of a cut ribbon of
glass, and an output image representing a classification of one or
more detected and identified defects. Image 80 includes an edge
surface image having a chip defect, hackle lines, and Wallner
lines, and a resultant image according to the above-noted
algorithm, which indicates such defects. Image 82 includes an edge
surface image having hackle lines, Wallner lines, and arrest lines,
and a resultant image according to the above-noted algorithm, which
indicates such defects. Images 84 and 86 each include an edge
surface image having chip defects, and a resultant image according
to the above-noted algorithm, which indicates such defects. Image
88 includes an edge surface image having chips, hackle lines, and
Wallner lines, and a resultant image according to the above-noted
algorithm, which indicates such defects. The processing results are
very promising, as the algorithm performed very well on the edge
surface images.
[0106] Again, the images of FIG. 12 are intended to illustrate the
classification of the defects by type (chip, hackle lines, Wallner
lines, and arrest lines) using shading (and/or color). However, due
to the limitations of the resolution of the images 80, 82, 84, 86,
and 88, the skilled artisan may envision that a color
representation of the defects may be: minor defects using dark
blue, unknown defects using medium blue, bright features using
light blue, dark features using light green, Wallner lines using
yellow, hackle lines using orange, arrest lines using bright red,
and chip defects using brown.
[0107] Although the disclosure herein has been described with
reference to particular embodiments, it is to be understood that
these embodiments are merely illustrative of the principles and
applications of the embodiments herein. It is therefore to be
understood that numerous modifications may be made to the
illustrative embodiments and that other arrangements may be devised
without departing from the spirit and scope of the present
application. For example, the various features may be combined as
set forth in the specific exemplary embodiments below.
Embodiment 1
[0108] A method, comprising: [0109] moving a glass web comprising a
length and a width transverse to the length from a source to a
destination in a transport direction along the length of the glass
web; [0110] cutting the glass web, at a cutting zone, along the
length of the glass web into at least first and second glass
ribbons as the glass web is moved in the transport direction from
the source to the destination such that respective first and second
edge surfaces are produced on the first and second glass ribbons;
and [0111] optically inspecting at least one of the first and
second edge surfaces in real-time as the first and second glass
ribbons are moved in the transport direction to the destination,
[0112] wherein the inspecting step includes: (i) taking at least
one image of the at least one of the first and second edge surfaces
as the first and second glass ribbons are moved in the transport
direction, (ii) extracting one or more features of the at least one
of the first and second edge surfaces from the at least one image,
and (iii) detecting one or more defects, and identifying one or
more types of the one or more defects based on the one or more
extracted features.
Embodiment 2
[0113] The method of embodiment 1, wherein the inspecting step
includes: [0114] directing incident light onto and through an
opposing edge surface of at least one of the first and second glass
ribbons that is laterally opposite to the at least one of the first
and second edge surfaces; [0115] propagating the light through the
at least one of the first and second glass ribbons, transversely
with respect to the transport direction, such that the light exits
through the at least one of the first and second edge surfaces; and
[0116] directing an optical axis of an imaging sensor substantially
perpendicularly toward the at least one of the first and second
edge surfaces, to receive the light exiting the at least one of the
first and second edge surfaces, such that the imaging sensor
produces the at least one image.
Embodiment 3
[0117] The method of embodiment 2, wherein the step of directing
the imaging sensor toward the at least one of the first and second
edge surfaces includes: [0118] monitoring a distance from the
imaging sensor and/or a reference position to the at least one of
the first and second edge surfaces as the first and second glass
ribbons of the glass web are moved in the transport direction to
the destination; and [0119] automatically adjusting a position of
focus of the imaging sensor as a function of the distance such that
the at least one image remains in focus.
Embodiment 4
[0120] The method of any one of embodiments 1-3, wherein the step
of detecting the one or more defects, and identifying the one or
more types of the one or more defects, includes: [0121] enhancing
one or more defect features as compared to background features in
the at least one image; [0122] applying a segmentation process to
the enhanced defect features to separate high contrast features
from lower contrast features, thereby producing a plurality of
segments; and [0123] extracting features from each of the plurality
of segments by analyzing each segment as to one or more of the
following features: [0124] (i) a total area of the segment, [0125]
(ii) an eccentricity and/or elongation of the segment, [0126] (iii)
a width of the segment, [0127] (iv) a height of the segment, and
[0128] (v) a fill ratio of the segment.
Embodiment 5
[0129] The method of embodiment 4, further comprising grouping at
least some of the segments together to form at least one aggregated
segment.
Embodiment 6
[0130] The method of embodiment 4 or embodiment 5, further
comprising determining and identifying the one or more types of the
one or more defects based on the analysis of the segments.
Embodiment 7
[0131] The method of embodiment 6, further comprising making a
determination that one or more of the segments represents a chip
when: [0132] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0133] (ii) an eccentricity and/or elongation of the one or
more segments is relatively low, within a range of relatively low
to relatively high, and [0134] (iii) a fill ratio of the one or
more segments is relatively high, within a range of relatively low
to relatively high.
Embodiment 8
[0135] The method of embodiment 6, further comprising making a
determination that one or more of the segments represent a hackle
line when: [0136] (i) a width of the one or more segments is
relatively small, within a range of relatively small to relatively
large, and [0137] (ii) a location of the one or more segments is
relatively close to a periphery of the edge surface.
Embodiment 9
[0138] The method of embodiment 6, further comprising making a
determination that one or more of the segments represent a Wallner
line when: [0139] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0140] (ii) an eccentricity and/or elongation of the one or
more segments is relatively high, within a range of relatively low
to relatively high, and [0141] (iii) a height of the one or more
segments is relatively small, within a range of relatively small to
relatively large.
Embodiment 10
[0142] The method of embodiment 6, further comprising making a
determination that one or more of the segments represent an arrest
line when: [0143] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0144] (ii) an eccentricity and/or elongation of the one or
more segments is relatively high, within a range of relatively low
to relatively high, [0145] (iii) a width of the one or more
segments is relatively small, within a range of relatively small to
relatively large, and [0146] (iv) a height of the one or more
segments is relatively large, within a range of relatively small to
relatively large.
Embodiment 11
[0147] The method of any one of embodiments 1-10, further
comprising automatically adjusting one or more parameters of the
step of cutting the glass web at the cutting zone based on the
detection and identification, wherein [0148] the step of cutting
the glass web at the cutting zone includes heating an elongated
zone of the glass web using a laser delivery apparatus followed by
cooling the heated portion of the glass web to propagate a fracture
in a direction opposite to the transport direction, thereby
producing the first and second ribbons; and [0149] the one or more
parameters of the step of cutting the glass web include a power
level of incident laser light from the laser delivery apparatus,
and a focus of the incident laser light from the laser delivery
apparatus.
Embodiment 12
[0150] An apparatus, comprising: [0151] a source apparatus
configured to supply a glass web, the glass web having a length and
a width transverse to the length; [0152] a transport mechanism
configured to move the glass web from the source apparatus to a
destination in a transport direction along the length of the glass
web; and [0153] a cutting mechanism configured to cut the glass
web, at a cutting zone, along the length into at least first and
second glass ribbons as the glass web is moved in the transport
direction from the source to the destination, such that respective
first and second edge surfaces are produced on the first and second
glass ribbons; and [0154] an inspection mechanism configured to
optically inspect at least one of the first and second edge
surfaces in real-time as the first and second glass ribbons of the
glass web are moved in the transport direction to the destination,
[0155] wherein the inspection mechanism is configured to execute
actions, including: (i) taking at least one image of the at least
one of the first and second edge surfaces as the first and second
glass ribbons are moved in the transport direction, (ii) extracting
one or more features of the at least one of the first and second
edge surfaces from the at least one image, and (iii) detecting one
or more defects, and identifying one or more types of the one or
more defects, based on the one or more extracted features.
Embodiment 13
[0156] The apparatus of embodiment 12, wherein the inspection
mechanism comprises: [0157] a light source configured to direct
incident light onto and through an opposing edge surface of at
least one of the first and second glass ribbons that is laterally
opposite to the at least one of the first and second edge surfaces,
such that the light propagates through the at least one of the
first and second glass ribbons, transversely with respect to the
transport direction, such that the light exits through the at least
one of the first and second edge surfaces; and [0158] an imaging
sensor comprising an optical axis directed substantially
perpendicularly toward the at least one of the first and second
edge surfaces, and configured to receive the light exiting the at
least one of the first and second edge surfaces, such that the
imaging sensor produces the at least one image.
Embodiment 14
[0159] The apparatus of embodiment 13, further comprising an
automatic focus mechanism comprising: [0160] a distance sensor
configured to monitor a distance from the imaging sensor and/or a
reference position to the at least one of the first and second edge
surfaces as the first and second glass ribbons of the glass web are
moved in the transport direction to the destination; and [0161] a
motion stage configured to automatically adjust a position of focus
of the imaging sensor as a function of the varying distance such
that the at least one image remains in focus.
Embodiment 15
[0162] The apparatus of any one of embodiments 12-14, wherein the
inspection mechanism includes a computer processor configured to
operate under the control of a computer program, which when
executed by the computer processor causes the computer processor to
carry out the actions of detecting the one or more defects, and
identifying the one or more types of the one or more defects, by:
[0163] enhancing one or more defect features as compared to
background features in the at least one image; [0164] applying a
segmentation process to the enhanced defect features to separate
high contrast features from lower contrast features, thereby
producing a plurality of segments; and [0165] extracting features
from each of the plurality of segments by analyzing each segment as
to one or more of the following features: [0166] (i) a total area
of the segment, [0167] (ii) an eccentricity and/or elongation of
the segment, [0168] (iii) a width of the segment, [0169] (iv) a
height of the segment, and [0170] (v) a fill ratio of the
segment.
Embodiment 16
[0171] The apparatus of embodiment 15, wherein the inspection
mechanism is further configured to carry out an action of grouping
at least some of the segments together to form at least one
aggregated segment.
Embodiment 17
[0172] The apparatus of embodiment 15 or embodiment 16, wherein the
inspection mechanism is further configured to carry out an action
of determining and identifying the one or more types of the one or
more defects based on the analysis of the segments.
Embodiment 18
[0173] The apparatus of embodiment 17, wherein the inspection
mechanism is further configured to carry out an action of making a
determination that one or more of the segments represent a chip
when: [0174] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0175] (ii) an eccentricity and/or elongation of the one or
more segments is relatively low, within a range of relatively low
to relatively high, and [0176] (iii) a fill ratio of the one or
more segments is relatively high, within a range of relatively low
to relatively high.
Embodiment 19
[0177] The apparatus of embodiment 17, wherein the inspection
mechanism is further configured to carry out an action of making a
determination that one or more of the segments represent a hackle
when: [0178] (i) a width of the one or more segments is relatively
small, within a range of relatively small to relatively large, and
[0179] (ii) a location of the one or more segments is relatively
close to a periphery of the edge surface.
Embodiment 20
[0180] The apparatus of embodiment 17, wherein the inspection
mechanism is further configured to carry out an action of making a
determination that one or more of the segments represent a Wallner
line when: [0181] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0182] (ii) an eccentricity and/or elongation of the one or
more segments is relatively high, within a range of relatively low
to relatively high, and [0183] (iii) a height of the one or more
segments is relatively small, within a range of relatively small to
relatively large.
Embodiment 21
[0184] The apparatus of embodiment 17, wherein the inspection
mechanism is further configured to carry out an action of making a
determination that one or more of the segments represent an arrest
line when: [0185] (i) a total area of the one or more segments is
relatively large, within a range of relatively small to relatively
large, [0186] (ii) an eccentricity and/or elongation of the one or
more segments is relatively high, within a range of relatively low
to relatively high, [0187] (iii) a width of the one or more
segments is relatively small, within a range of relatively small to
relatively large, and [0188] (iv) a height of the one or more
segments is relatively large, within a range of relatively small to
relatively large.
Embodiment 22
[0189] The apparatus of any one of embodiments 12-21, further
comprising a feedback mechanism configured to automatically adjust
one or more parameters of the cutting mechanism and of cutting the
glass web at the cutting zone based on the detection and
identification, and wherein [0190] the cutting mechanism includes a
laser delivery apparatus configured to heat an elongated zone of
the glass web and a cooling fluid source configured to cool the
heated portion of the glass web to propagate a fracture in a
direction opposite to the transport direction and cut the glass
web, thereby producing the first and second ribbons; and [0191] the
one or more parameters of the cutting mechanism include a power
level of incident laser light from the laser delivery apparatus,
and a focus of the incident laser light from the laser delivery
apparatus.
* * * * *